摩尔线程王华：万卡训练中，最危险的往往是「不报错」丨GAIR 2025

Core Insights - The article discusses the challenges and solutions related to large-scale training practices in AI, particularly focusing on the necessity of massive GPU clusters for training large models [4][6][7]. Group 1: Importance of Large-Scale Training - Large-scale training, specifically with tens of thousands of GPUs, has become a necessary condition for developing large models, as the computational demands have reached unprecedented levels [6][7]. - The computational requirements for mainstream models like DeepSeek and domestic trillion-parameter models are around 10^24 FLOPs, while larger models like Grok4 and GPT-5 may require up to 10^26 FLOPs [7][8][9]. Group 2: Challenges in Large-Scale Training - The transition to large-scale training introduces new challenges such as node failures, performance fluctuations, and communication/storage bottlenecks, which were manageable at smaller scales but become critical at larger scales [4][12]. - Stability and controllability are significant challenges, with issues like silent data errors and system hangs posing risks to training processes [18][20][23]. Group 3: Solutions and Innovations - The company has developed a comprehensive software stack to enhance training efficiency, including a scheduling system, MUSA platform for compatibility, and various training tools optimized for popular frameworks [10][12]. - Innovations such as asynchronous checkpointing and automated pre-training checks have been implemented to minimize downtime and improve overall training efficiency [17][15]. - A monitoring system has been established to detect slow nodes and silent data errors, ensuring that training processes remain stable and efficient [19][20][26]. Group 4: Future Directions - The article emphasizes the importance of continuous improvement and adaptation in training practices, suggesting that the experiences and solutions developed can serve as a reference for other companies and institutions aiming to engage in large-scale training [28].